{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import warnings\n",
    "%matplotlib inline\n",
    "\n",
    "#Data import\n",
    "data_training_FD2 = pd.read_csv('train_FD002.txt',sep=\" \", header=None)\n",
    "data_training_FD2.head()\n",
    "engine_cycle = ['engine_id','cycle']\n",
    "settings = ['setting1','setting2','setting3']\n",
    "sensors = ['s%s'%i for i in range(1,22)]\n",
    "nan_cols = ['NaN1','NaN2']\n",
    "data_training_FD2.columns = engine_cycle + settings + sensors + nan_cols\n",
    "data_training_FD2 = data_training_FD2.drop(['NaN1','NaN2'],axis=1)\n",
    "\n",
    "#RUL\n",
    "RULtraining = []\n",
    "for unit in range(1,data_training_FD2['engine_id'].unique().shape[0]):\n",
    "    RULtraining.append(data_training_FD2[data_training_FD2['engine_id'] == unit]['cycle'].max())\n",
    "\n",
    "#Set a look-back time range to train the model\n",
    "dt = 10\n",
    "\n",
    "#Load training data\n",
    "Xtraining_to_testing = np.load('Xtraining_to_testing_dt_10.npy')\n",
    "Ytraining_to_testing = np.load('Ytraining_to_testing_dt_10.npy')\n",
    "engines_training_to_testing = np.load('engines_training_to_testing_dt_10.npy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "#Import modules\n",
    "import keras\n",
    "from keras.models import Sequential\n",
    "from keras.layers import LSTM\n",
    "from keras.layers import Dense\n",
    "\n",
    "#Import trained model\n",
    "from keras.models import load_model\n",
    "model = load_model('model_dt_10.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#Model evaluation\n",
    "engines_to_check = [1,3,5,7,8,9]\n",
    "\n",
    "prediction, real = [], []\n",
    "for engine in engines_to_check:\n",
    "    if type(engines_training_to_testing) == list:\n",
    "        location = engines_training_to_testing.index(engine)\n",
    "    else:\n",
    "        location = engines_training_to_testing.tolist().index(engine)\n",
    "    Xtest_engine = Xtraining_to_testing[location]\n",
    "    prediction_engine = model.predict(Xtest_engine)\n",
    "    prediction.append(prediction_engine.reshape(prediction_engine.shape[0]))\n",
    "    real.append(Ytraining_to_testing[location])\n",
    "\n",
    "#iNormalization\n",
    "time_of_event_max = 378.0\n",
    "for index in range(len(real)): # for every engine\n",
    "    prediction[index] = prediction[index]*time_of_event_max\n",
    "    real[index] = real[index]*time_of_event_max"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "# put this function in util.py or generate visualization.py\n",
    "def plot_RUL(engine, X, engines_to_check, prediction, real, RULtraining, dt):\n",
    "    plt.figure(figsize=(12,3))\n",
    "    location = engines_to_check.index(engine)\n",
    "    Yprediction = prediction[location]\n",
    "    cycle = X[X['engine_id']==engine].cycle[dt-1:]\n",
    "    Yreal = real[location]\n",
    "    AverageRUL = range(0,-len(Yreal),-1) + np.mean(RULtraining) - dt\n",
    "    print cycle[:5] \n",
    "    print AverageRUL[:5]\n",
    "    \n",
    "    plt.plot(cycle,AverageRUL,'k.',label='Average time-to-failure')\n",
    "    plt.plot(cycle,Yreal,'b-',label='True time-to-failure')\n",
    "    plt.plot(cycle,Yprediction,'r-',label='Predicted time-to-failure')\n",
    "    plt.title('Engine: %d' %engine)\n",
    "    plt.xlabel('Cycle',fontsize=12)\n",
    "    plt.legend(loc='upper right')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9     10\n",
      "10    11\n",
      "11    12\n",
      "12    13\n",
      "13    14\n",
      "Name: cycle, dtype: int64\n",
      "[ 206.34362934  205.34362934  204.34362934  203.34362934  202.34362934]\n"
     ]
    },
    {
     "data": {
      "image/png": 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szj47yzY7K9sAzT4rpdKndOnSzJs3j169enHt2jUAJk6cSM2aNZk1axYPPPAA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\nTggPhw8+MG1E1qwxUb9SKltkRaeNLl2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6VpqZzQLOBlqY2Rbgdsr5WeDuq83s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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12c75ddd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_RUL(engine = 1, X = data_training_FD2, engines_to_check = engines_to_check,\n",
    "         prediction = prediction, real = real, RULtraining = RULtraining, dt = dt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
